Mathematics

# MATH 4570: Matrix Methods in Data Analysis and Machine Learning

Lecture - 4 credits

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- Introduces concepts and methods of linear algebra for understanding and creating machine learning and deep learning algorithms.
- Topics include various matrix factorizations, symmetric positive definite matrices, inner product spaces, matrix calculus, applications to probability and statistics, and optimization in high-dimensional spaces.
- Explores the mathematics behind data analysis, machine learning, and deep learning, including gradient descents, Newton's methods, principal components analysis, linear regression and linear methods in classification, neural networks, and convolutional neural networks.
- Offers students opportunities to learn and practice Python skills with labs and the final project.

Introduces concepts and methods of linear algebra for understanding and creating machine learning and deep learning algorithms.

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